Bad weather Kelheim Demo

Research Meteostat

After some researches about meteostat data nearest station in DE that belongs to Kelheim region are:

• “Mallersdorf-Pfaffenberg/Niederbayern” with an id: “D3147”
• “Neumarkt / Höhenberg” with and id: “69110”
• Uebungsdorf / Emhof

there are many of them so I am starting to think about extracting all from the Bayern or extract the nearest from longtitude/latitude point with the Kelheim shapefile(using json and Euclid distances)

Kelheim has no weather station, but it could be reconstructed with 2 other

Hohenfels with id: “10775” and Ingolstadt with id:“10860” kelheim_data = {weight1}x{hohenfels} + {weight2}x{inglstadt}

Also this site shows, that there are many of the Kelheim stations in this area, but meteostat doesn’t contain them https://www.wunderground.com/dashboard/pws/IKELHE5

Research Weatherstack

weatherstack_kelheim = read_delim("data/Kelheim_weather_since_july_2008.csv",delim = ",")

Rows: 120312 Columns: 6── Column specification ─────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr  (1): description
dbl  (4): hour, precip, visibility, totalsnow_daily
date (1): date
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

print(weatherstack_kelheim)

What to take as a reffer point isn’t clear because of the date(before/after covid) and weather type (sunny,clear,temperature) Also there is no temperature in it :/

Import mobility from Google

global_mobility = read_delim("data/Global_Mobility_Report.csv",",")
de_mobility = global_mobility %>% filter(country_region_code == "DE")

print(unique(de_mobility$sub_region_1))

 [1] NA                       "Baden-Württemberg"      "Bavaria"                "Berlin"                
 [5] "Brandenburg"            "Bremen"                 "Hamburg"                "Hessen"                
 [9] "Lower Saxony"           "Mecklenburg-Vorpommern" "North Rhine-Westphalia" "Rhineland-Palatinate"  
[13] "Saarland"               "Saxony"                 "Saxony-Anhalt"          "Schleswig-Holstein"    
[17] "Thuringia"             

bavaria_mobility = de_mobility %>% filter(sub_region_1 == "Bavaria")

As we can see the most precise region to filter data from is Bavaria :/

Relevant data for the , mobility

bavaria_mobility = de_mobility %>% filter(sub_region_1 == "Bavaria")
bavaria_mobility = bavaria_mobility %>% select(country_region,sub_region_1,date,residential_percent_change_from_baseline) %>%
  mutate(residential_percent_change_from_baseline = -residential_percent_change_from_baseline,
         source = "Google")%>%
  rename(BundeslandID = sub_region_1,not_at_home_change = residential_percent_change_from_baseline)
bavaria_mobility = bavaria_mobility %>% select(date,BundeslandID,not_at_home_change,source)
#Need to filter out weekends

plt = ggplot(bavaria_mobility)+
  geom_point(aes(x = date,y = not_at_home_change))
ggplotly(plt)

Import mobility from Senozon

snz_mobility = read_delim("data/mobilityData_OverviewBL_weekly.csv",";")

Rows: 2108 Columns: 4── Column specification ─────────────────────────────────────────────────────────────────────────────────
Delimiter: ";"
chr (1): BundeslandID
dbl (3): date, outOfHomeDuration, percentageChangeComparedToBeforeCorona
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

snz_mobility = snz_mobility %>% filter(BundeslandID == "Bayern") %>% mutate(source = "senozon") %>% select(-outOfHomeDuration) %>% rename(not_at_home_change = percentageChangeComparedToBeforeCorona)
snz_mobility$date = as.Date(strptime(snz_mobility$date,"%Y%m%d"))
plt = ggplot(snz_mobility)+
  geom_point(aes(x = date,y = not_at_home_change))
ggplotly(plt)

Aggregate 2 sources

mob_joined = rbind(snz_mobility,bavaria_mobility)

plt = ggplot(mob_joined)+
  geom_point(aes(x = date,y = not_at_home_change,color = source))
ggplotly(plt)

NA